Monitor the Environment in Azure Machine Learning

1 hour
  • 2 Learning Objectives

About this Hands-on Lab

In this hands-on lab, you will become familiar with monitoring models in Azure Machine Learning.

Learning Objectives

Successfully complete this lab by achieving the following learning objectives:

Monitor Model Telemetry

Use Azure Application Insights to monitor activity for a model service endpoint.

Monitor Data Drift

Use a data drift monitor to analyze the effectiveness of a model over time.

Additional Resources

Lab Scenario

In this hands-on lab scenario, you are a Data Scientist for Awesome Company. Recently, the company has been using the Azure Machine Learning service to implement and run their machine learning workloads concerning diabetes research. With these services now being deployed to production, there is a need to monitor both the model telemetry and data drift.

To accomplish your goal, the following should be completed:

  • Use Azure Application Insights to monitor activity for a model service endpoint
  • Configure data drift monitoring for a dataset

Lab Setup

To utilize the preconfigured Jupyter notebook, clone the repo from GitHub Repo.

                                                                             Use the Note:  For the notebook compute instance, please use the **Standard_D2s_v3** machine type in this lab.

What are Hands-on Labs

Hands-on Labs are real environments created by industry experts to help you learn. These environments help you gain knowledge and experience, practice without compromising your system, test without risk, destroy without fear, and let you learn from your mistakes. Hands-on Labs: practice your skills before delivering in the real world.

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Psst…this one if you’ve been moved to ACG!

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